fix more comma errors

materialandmethods.typ

@@ -249,7 +249,7 @@ For this bachelor thesis the ResNet-50 architecture was used to predict the corr
 
 === P$>$M$>$F
 // https://arxiv.org/pdf/2204.07305
-P>P>F (Pre-training > Meta-training > Fine-tuning) is a three-stage pipeline designed for few-shot learning.
+P>M>F (Pre-training > Meta-training > Fine-tuning) is a three-stage pipeline designed for few-shot learning.
 It focuses on simplicity but still achieves competitive performance.
 The three stages convert a general feature extractor into a task-specific model through fine-tuned optimization.
 #cite(<pmfpaper>)
@@ -309,7 +309,7 @@ Future research could focus on exploring faster and more efficient methods for f
 
 === CAML <CAML>
 // https://arxiv.org/pdf/2310.10971v2
-CAML (Context aware meta learning) is one of the state-of-the-art methods for few-shot learning.
+CAML (Context-Aware Meta-Learning) is one of the state-of-the-art methods for few-shot learning.
 It consists of three different components: a frozen pre-trained image encoder, a fixed Equal Length and Maximally Equiangular Set (ELMES) class encoder and a non-causal sequence model.
 This is a universal meta-learning approach.
 That means no fine-tuning or meta-training is applied for specific domains.~#cite(<caml_paper>)